About one year ago, British newspaper The Guardian ran an article titled A robot wrote this entire article. Are you scared yet, human?, written by an Artificial Intelligence (AI)-enabled robot called GPT-3 (Generative Pre-trained Transformer 3). It is an autoregressive language model that uses deep learning to produce human-like text. GPT-3 was fed a short introduction and was instructed to write an op-ed of around 500 words in simple language, focusing on why humans have nothing to fear from AI. In response, it produced eight different essays. The Guardian picked the best parts of each and ran the edited piece.

Scientists use super-resolution microscopy to study previously undiscovered cellular worlds, revealing nanometre-scale details inside cells. The method revolutionised light microscopy and earned its inventors the 2014 Nobel Prize in Chemistry. Single-molecule localisation microscopy (SMLM) is a type of super-resolution microscopy. It involves labelling proteins of interest with fluorescent molecules and using light to activate only a few molecules at a time. Using this method, multiple images of the same sample are acquired.

In this talk I will review the development of commercially successful Knowledge Representation and Reasoning (KRR) systems and their genesis in foundational research. I will trace the evolution of KRR systems from logical and algorithmic foundations, through academic prototypes and standardisation to robust and scalable systems that power applications in areas as diverse as search, healthcare, financial services and manufacturing. I will discuss the barriers and milestones encountered along the journey, and lessons learned about the exploitation of research. Multi-agent systems first emerged as a research topic in the late 1980s. A key driver behind the emergence of the field was the idea of building systems that actively worked on behalf of human users in the pursuit of those users' goals.

Robotics Field has revolutionized today's world. Sophia Humanoid robot is attending television interviews, appearing on the cover of ELLE magazine. She was imitated on HBO as the first non-human "innovation champion" of the UN. In a tech conference held soon after its awakening, the Kingdom of Saudi Arabia even gave citizenship to Sophia. A humanoid robot is a robot with its body shape built to resemble the human body. The design may be for functional purposes, such as interacting with human tools and environments, for experimental purposes, such as the study of bipedal locomotion, or for other purposes.

The Van Wijngaarden Awards 2021 are awarded to computer scientist Marta Kwiatkowska and mathematician Susan A. Murphy for the numerous and highly significant contributions they made to their respective research areas: preventing software faults and improving decision making in health. The five-yearly award is established by CWI, the national research institute for mathematics and computer science in the Netherlands, and is named after former CWI director Aad van Wijngaarden. The winners receive the prize during a festive soirée on 18 November in Amsterdam. Marta Kwiatkowska (University of Oxford) is a computer scientist who pioneered research on modelling, verification, and synthesis of probabilistic systems. She led the development of the highly influential PRISM probabilistic model checker, which is widely used for research and teaching and which has been downloaded over 80,000 times. In her research Kwiatkowska showed the relevance of PRISM by applying it in several areas, including ubiquitous computing, system biology, DNA computing, and most recently, safety for AI.

We are often faced with the problem of how to evaluate the quality of a large software system. The primary evaluation metric is definitely functionality and whether the software meets the main requirements (do right things). If there are multiple technical paths to achieve the same functionality, people tend to choose the more simple approach. Occam's Razor guideline "Entities should not be multiplied unnecessarily" sums up very well the preference for simplicity, which is to counter the challenge of complexity. The underlying logic of this preference is: "simplicity does things right. In the 1960s, the Software Crisis (Software crisis -- Wikipedia) was once called because software development could not keep up with the development of hardware and the growth in complexity of real problems and could not be delivered in the planned time. Fred Brooks, a Turing Award winner who led the development of System/360 and OS/360 at IBM, described the plight of a giant beast dying in a tar pit in the bible of software engineering, "The Mythical Man-Month", to draw an analogy with software developers who are mired in software complexity and cannot get out. He also introduced the famous Brooks' Law, "Adding people to a project that is behind schedule only makes it more behind schedule". In his paper "No Silver Bullet -- Essence and Accidents of Software Engineering," he further divides the difficulties of software development into essential and episodic and identifies several major causes of essential difficulties: complexity, invisibility, conformity, and changeability, with complexity leading the way. In 2006, a paper entitled "Out of the Tar Pit" echoed Brooks. This paper argues that complexity is the only major difficulty preventing successful large-scale software development, and that several of the other causes Brooks suggests are secondary disasters resulting from unmanageable complexity, with complexity being the root cause. This paper, too, cites several Turing Award winners for their excellent discussions of complexity. "…we have to keep it crisp, disentangled, and simple if we refuse to be crushed by the complexities of our own making…" "The general problem with ambitious systems is complexity.", "…it is important to emphasize the value of simplicity and elegance, for complexity has a way of compounding difficulties" "there is a desperate need for a powerful methodology to help us think about programs.

The commonly used latent space embedding techniques, such as Principal Component Analysis, Factor Analysis, and manifold learning techniques, are typically used for learning effective representations of homogeneous data. However, they do not readily extend to heterogeneous data that are a combination of numerical and categorical variables, e.g., arising from linked GPS and text data. In this paper, we are interested in learning probabilistic generative models from high-dimensional heterogeneous data in an unsupervised fashion. The learned generative model provides latent unified representations that capture the factors common to the multiple dimensions of the data, and thus enable fusing multimodal data for various machine learning tasks. Following a Bayesian approach, we propose a general framework that combines disparate data types through the natural parameterization of the exponential family of distributions. To scale the model inference to millions of instances with thousands of features, we use the Laplace-Bernstein approximation for posterior computations involving nonlinear link functions. The proposed algorithm is presented in detail for the commonly encountered heterogeneous datasets with real-valued (Gaussian) and categorical (multinomial) features. Experiments on two high-dimensional and heterogeneous datasets (NYC Taxi and MovieLens-10M) demonstrate the scalability and competitive performance of the proposed algorithm on different machine learning tasks such as anomaly detection, data imputation, and recommender systems.

Read Leonie’s story of how she did her research about aeroelasticity at MIT & TU Berlin, how she does data analysis in aerospace engineering, and you will know more about her new challenges as a Machine Learning engineer in the aerospace industry.

As industrial machines are becoming more connected and flexible, the process of building and commissioning the machine is also getting smarter. Machines are built now using artificial intelligence, digital twins, and augmented reality. We caught up with Rahul Garg, VP of industrial machinery and mid-market program at Siemens Digital Industries Software. Garg explained the process of creating smart industrial machines using advanced technology. Design News: Is artificial intelligence becoming a major factor in building industrial machines?

Modern security information and event management and intrusion detection systems leverage ML to correlate network features, identify patterns in data and highlight anomalies corresponding to attacks. Security researchers spend many hours understanding these attacks and trying to classify them into known kinds like port sweep, password guess, teardrop, etc. However, due to the constantly changing attack landscape and the emergence of advanced persistent threats (APTs), hackers are continuously finding new ways to attack systems. A static list of classification of attacks will not be able to adapt to new and novel tactics adopted by adversaries. Also, due to the constant flow of alarms generated by multiple sources in the network, it becomes difficult to distinguish and prioritize particular types of attacks--the classic alarm flooding problem.